The image quality and the range at which a camera could be used have long been primarily dependent on internal physical parameters such as the size and quality of the lenses, the resolution and accuracy in the detector, the absence of vibrations etc. These parameters are restrictive even today. When seeking to record images at longer ranges in hot terrains, especially in desert landscapes and over ocean surfaces, account needs to be taken of yet another parameter, namely the phenomenon of turbulence. The phenomenon may be discerned, for example, above a motorway on a hot summer's day, when the air seems to quiver. In such environments the turbulence phenomenon imposes direct limits on what can be imaged. Regardless of how sharply and accurately modern cameras present the ray path from the imaged object, the image lacks the information sought, since it gets lost before the ray path even reaches the camera. One specific sphere of application that might be mentioned is border surveillance in a desert landscape, where the turbulence phenomenon greatly limits the ability of the camera to form an image of distant objects and to distinguish real movements in a video clip, especially those of limited extent.
The turbulence phenomenon originates in variations in the refractive index of the air, which varies with the atmospheric temperature. When the sun heats up an area of land mass the air above this is also heated. The warm air will then rise upwards, like bubbles from a diver. Just like the air bubbles from a diver, these bubbles will also be broken down into smaller and smaller intact masses. When the ray path passes through an area with ‘bubbles drifting around’ having a varying refractive index, its direction will be modified in just the same way as when it passes through a lens. In most cases the bubbles are small in relation to the view, so that different parts of the ray path are affected differently. Since they also move, the ray path will also be refracted differently from one frame to the next. This is the reason why the aforementioned quiver occurs and why over time varying deformations of the object in the image occur. The turbulence also causes contours in the image to become blurred.
A known method of restoring image information caused by atmospheric turbulence is to perform temporal median filtering of an image sequence pixel by pixel. Reference may be made in this context to the article by J. Gilles: Restoration algorithms and system performance evaluation for active imagers, Proc. of SPIE Vol. 6739, pages 67390B-1-67390B-8. The known method cited above is primarily suited to stabilizing a basically fixed view.
Methods have also been proposed for detecting movement in which sub-frames of the image are supplied with calculated movement vectors by analysing where they best fit into a reference view, see for example D. H. Frakes, J. W. Monaco, M. J. T. Smith; Suppression of Atmospheric turbulence in video using an adaptive control grid interpolation approach; Proc. IEEE Internal Conference on Acoustics, Speech and Signal Processing, vol. 3, pp 1881-1884, November 2001. These proposed known methods are calculation-intensive and also require that the majority of the sub-frames contain homogeneous linear movements and, for example, that an individual who is turning around on the spot should not be detected as a moving object. After extensive calculations the latter principle of the method allows compensation for turbulence without removing actual movement of large objects that move linearly.